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Showing papers on "Motion planning published in 2004"


Book
05 Mar 2004
TL;DR: Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.
Abstract: Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.

2,414 citations


Journal ArticleDOI
01 Feb 2004
TL;DR: A novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments and results show that the proposed model is capable of planning collision-free complete coverage robot paths.
Abstract: Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.

397 citations


Journal ArticleDOI
TL;DR: A genetic algorithm for path planning of an autonomous underwater vehicle in an ocean environment characterized by strong currents and enhanced space-time variability is proposed, suitable for situations in which the vehicle has to operate energy-exhaustive missions.
Abstract: This paper proposes a genetic algorithm (GA) for path planning of an autonomous underwater vehicle in an ocean environment characterized by strong currents and enhanced space-time variability. The goal is to find a safe path that takes the vehicle from its starting location to a mission-specified destination, minimizing the energy cost. The GA includes novel genetic operators that ensure the convergence to the global minimum even in cases where the structure (in space and time) of the current field implies the existence of different local minima. The performance of these operators is discussed. The proposed algorithm is suitable for situations in which the vehicle has to operate energy-exhaustive missions.

365 citations


Journal ArticleDOI
TL;DR: The main contribution of this paper is to lay down and explore the novel concept of inevitable collision state of a robotic system subject to sensing constraints in a partially known environment (i.e. that may contain unexpected obstacles).
Abstract: An inevitable collision state for a robotic system can be defined as a state for which, no matter what the future trajectory followed by the system is, a collision with an obstacle eventually occurs. An inevitable collision state takes into account the dynamics of both the system and the obstacles, fixed or moving. The main contribution of this paper is to lay down and explore this novel concept (and the companion concept of inevitable collision obstacle). Formal definitions of the inevitable collision states and obstacles are given. Properties fundamental for their characterization are established. This concept is very general, and can be useful both for navigation and motion planning purposes (for its own safety, a robotic system should never find itself in an inevitable collision state). To illustrate the interest of this concept, it is applied to a problem of safe motion planning for a robotic system subject to sensing constraints in a partially known environment (i.e. that may contain unexpected obst...

353 citations


Journal ArticleDOI
TL;DR: Two demonstrator platforms for a robotic home assistant—called Care-O-bot—were designed and implemented at Fraunhofer IPA, Stuttgart and a new method for sensor based manipulation using a tilting laser scanner and camera integrated in the head of the robot has been implemented.
Abstract: Technical aids allow elderly and handicapped people to live independently and supported in their private homes for a longer time. As a contribution to such technological solutions, two demonstrator platforms for a robotic home assistant—called Care-O-bot—were designed and implemented at Fraunhofer IPA, Stuttgart. Whereas Care-O-bot I is only a mobile platform with a touch screen, Care-O-bot II is additionally equipped with adjustable walking supporters and a manipulator arm. It has the capability to navigate autonomously in indoor environments, be used as an intelligent walking support, and execute manipulation tasks. The control software of Care-O-bot II runs on two industrial PCs and a hand-held control panel. The walking aid module is based on sensors in the walking aid handles and on a dynamic model of conventional walking aids. In “direct mode”, the user can move along freely with the robot whereas obstacles are detected and avoided. In “planned mode”, he can specify a target and be lead there by the robotic assistant. Autonomous planning and execution of complex manipulation tasks is based on a symbolic planner and environmental information provided in a database. The user input (graphical and speech input) is transferred to the task planner and adequate actions to solve the task (sequence of motion and manipulation commands) are created. A new method for sensor based manipulation using a tilting laser scanner and camera integrated in the head of the robot has been implemented. Additional sensors in the robot hand increase the grasping capabilities. The walking aid has been tested with elderly users from an assisted living facility and a nursery home. Furthermore, the execution of fetch and carry tasks has been implemented and tested in a sample home environment.

331 citations


Journal ArticleDOI
TL;DR: A general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches.
Abstract: This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems.

309 citations


Journal ArticleDOI
TL;DR: A robust recursive design technique is developed for uncertain nonlinear plants in vectorial strict feedback form that bridges the geometric design with the speed assignment.

306 citations


Proceedings ArticleDOI
06 Jul 2004
TL;DR: This paper examines some of the implementation issues in rigid body path planning and presents techniques which have been found to be effective experimentally for Rigid Body path planning.
Abstract: Important implementation issues in rigid body path planning are often overlooked. In particular, sampling-based motion planning algorithms typically require a distance metric defined on the configuration space, a sampling function, and a method for interpolating sampled points. The configuration space of a 3D rigid body is identified with the Lie group SE(3). Defining proper metrics, sampling, and interpolation techniques for SE(3) is not obvious, and can become a hidden source of failure for many planning algorithm implementations. This paper examines some of these issues and presents techniques which have been found to be effective experimentally for Rigid Body path planning.

289 citations


Proceedings ArticleDOI
27 Sep 2004
TL;DR: A new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom with a fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot.
Abstract: To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion on natural surfaces has to cope with yaw, pitch and roll angles, turning pose estimation into a problem in six mathematical dimensions. A fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot. Finally, consistent 3D maps are generated using a global relaxation. The algorithms have been tested with 3D scans taken in the Mathies mine, Pittsburgh, PA. Abandoned mines pose significant problems to society, yet a large fraction of them lack accurate 3D maps.

282 citations


Proceedings ArticleDOI
07 Jun 2004
TL;DR: In this article, a knowledge-based GA for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAs.
Abstract: In this paper, a knowledge based genetic algorithm (GA) for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAs. The proposed knowledge based genetic algorithm incorporates the domain knowledge into its specialized operators, where some also combine a local search technique. The proposed genetic algorithm also features a unique and simple path representation and a simple but effective evaluation method. The knowledge based genetic algorithm is capable of finding an optimal or near-optimal robot path in both complex static and dynamic environments. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed GA for solving robot path planning problem is demonstrated by a comparison study.

252 citations


Proceedings ArticleDOI
07 Jun 2004
TL;DR: A new approach to find energy-efficient motion plans for mobile robots by model the relationship of motors' speed and their power consumption with polynomials and comparing the energy consumption of different routes at different velocities.
Abstract: This paper presents a new approach to find energy-efficient motion plans for mobile robots. Motion planning has two goals: finding the routes and determining the velocities. We model the relationship of motors' speed and their power consumption with polynomials. The velocity of the robot is related to its wheels' velocities by performing a linear transformation. We compare the energy consumption of different routes at different velocities and consider the energy consumed for acceleration and turns. We use experiment-validated simulation to demonstrate up to 51% energy savings for searching an open area.

Journal ArticleDOI
TL;DR: A computationally inexpensive, yet high performance trajectory generation algorithm for omnidirectional vehicles that is based on a small number of evaluations of simple closed-form expressions and is thus extremely efficient.

Journal ArticleDOI
01 Aug 2004
TL;DR: This paper explores an approach for animating characters manipulating objects that combines the power of path planning with the domain knowledge inherent in data-driven, constraint-based inverse kinematics.
Abstract: Even such simple tasks as placing a box on a shelf are difficult to animate, because the animator must carefully position the character to satisfy geometric and balance constraints while creating motion to perform the task with a natural-looking style. In this paper, we explore an approach for animating characters manipulating objects that combines the power of path planning with the domain knowledge inherent in data-driven, constraint-based inverse kinematics. A path planner is used to find a motion for the object such that the corresponding poses of the character satisfy geometric, kinematic, and posture constraints. The inverse kinematics computation of the character's pose resolves redundancy by biasing the solution toward natural-looking poses extracted from a database of captured motions. Having this database greatly helps to increase the quality of the output motion. The computed path is converted to a motion trajectory using a model of the velocity profile. We demonstrate the effectiveness of the algorithm by generating animations across a wide range of scenarios that cover variations in the geometric, kinematic, and dynamic models of the character, the manipulated object, and obstacles in the scene.

Proceedings ArticleDOI
06 Jul 2004
TL;DR: The new multi-robot coverage algorithm uses the same planar cell-based decomposition as the single robot approach, but provides extensions to handle how teams of robots cover a single cell and how teams are allocated among cells.
Abstract: This paper presents an algorithm for the complete coverage of free space by a team of mobile robots. Our approach is based on a single robot coverage algorithm, which divides the target two-dimensional space into regions called cells, each of which can be covered with simple back-and-forth motions; the decomposition of free space in a collection of such cells is known as Boustrophedon decomposition. Single robot coverage is achieved by ensuring that the robot visits every cell. The new multi-robot coverage algorithm uses the same planar cell-based decomposition as the single robot approach, but provides extensions to handle how teams of robots cover a single cell and how teams are allocated among cells. This method allows planning to occur in a two-dimensional configuration space for a team of N robots. The robots operate under the restriction that communication between two robots is available only when they are within line of sight of each other.

Proceedings ArticleDOI
10 Nov 2004
TL;DR: This paper presents a resolution complete planner for a subclass of NAMO problems, which takes advantage of the navigational structure through state-space decomposition and heuristic search and presents a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.
Abstract: In this paper, we address the problem of navigation among movable obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path Simpler problems have been shown to be P-SPACE hard For real-world scenarios with large numbers of movable obstacles, complete motion planning techniques are largely intractable This paper presents a resolution complete planner for a subclass of NAMO problems Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain We demonstrate real-time results for spaces that contain large numbers of movable obstacles We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain

Journal ArticleDOI
TL;DR: A method of planning motion for formations of mobile robots with non-holonomic constraints is presented, which allows a certain class of formations to be maintained while the group as a whole exhibits motion.

Proceedings ArticleDOI
06 Jul 2004
TL;DR: Methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment are presented, based on plane extraction from data captured by a stereo-vision system that has been developed specifically forQRIO.
Abstract: This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.

Proceedings ArticleDOI
29 Nov 2004
TL;DR: In this article, a receding horizon strategy is presented with hard terminal constraints that guarantee feasibility of the MILP problem at all future time steps, and the trajectory computed at each iteration is constrained to end in a so called basis state, in which the vehicle can safely remain for an indefinite period of time.
Abstract: This paper extends a recently developed approach to optimal path planning of autonomous vehicles, based on mixed integer linear programming (MILP), to account for safety. We consider the case of a single vehicle navigating through a cluttered environment which is only known within a certain detection radius around the vehicle. A receding horizon strategy is presented with hard terminal constraints that guarantee feasibility of the MILP problem at all future time steps. The trajectory computed at each iteration is constrained to end in a so called basis state, in which the vehicle can safely remain for an indefinite period of time. The principle is applied to the case of a UAV with limited turn rate and minimum speed requirements, for which safety conditions are derived in the form of loiter circles. The latter need not be known ahead of time and are implicitly computed online. An example scenario is presented that illustrates the necessity of these safety constraints when the knowledge of the environment is limited and/or hard real-time restrictions are given.

Proceedings ArticleDOI
06 Jul 2004
TL;DR: Ridid body movements, maze like problems as well as path planning problems for chain-like robotic platforms have been solved successfully using the proposed algorithm.
Abstract: We present a new approach to path planning in high-dimensional static configuration spaces. The concept of cell decomposition is combined with probabilistic sampling to obtain a method called probabilistic cell decomposition (PCD). The use of lazy evaluation techniques and supervised sampling in important areas leads to a very competitive path planning method. It is shown that PCD is probabilistic complete, PCD is easily scalable and applicable to many different kinds of problems. Experimental results show that PCD performs well under various conditions. Rigid body movements, maze like problems as well as path planning problems for chain-like robotic platforms have been solved successfully using the proposed algorithm.

Journal ArticleDOI
01 Feb 2004
TL;DR: A recurrent neural network is developed and applied for kinematic control of redundant manipulators with obstacle avoidance capability and an improved problem formulation is proposed that the collision-avoidance requirement is represented by dynamically-updated inequality constraints.
Abstract: One important issue in the motion planning and control of kinematically redundant manipulators is the obstacle avoidance. In this paper, a recurrent neural network is developed and applied for kinematic control of redundant manipulators with obstacle avoidance capability. An improved problem formulation is proposed in the sense that the collision-avoidance requirement is represented by dynamically-updated inequality constraints. In addition, physical constraints such as joint physical limits are also incorporated directly into the formulation. Based on the improved problem formulation, a dual neural network is developed for the online solution to collision-free inverse kinematics problem. The neural network is simulated for motion control of the PA10 robot arm in the presence of point and window-shaped obstacle.

Book
08 Jul 2004
TL;DR: This monograph discusses issues related to estimation, control, and motion planning for mobile robots operating in rough terrain, with particular attention to planetary exploration rovers.
Abstract: This monograph discusses issues related to estimation, control, and motion planning for mobile robots operating in rough terrain, with particular attention to planetary exploration rovers. Rough terrain roboticsis becoming increasingly important in space exploration, and industrial applications. However, most current motion planning and control algorithms are not well suited to rough terrain mobility, since they do not consider the physical characteristics of the rover and its environment. Specific addressed topics are: wheel terrain interaction modeling, including terrain parameter estimation and wheel terrain contact angle estimation; rough terrain motion planning; articulated suspension control; and traction control. Simulation and experimental results are presented that show that the desribed algorithms lead to improved mobility for robotic systems in rough terrain.

Proceedings ArticleDOI
19 Jun 2004
TL;DR: This work presents results of their work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space.
Abstract: This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

Proceedings ArticleDOI
28 Sep 2004
TL;DR: Simulation results show that this combination of techniques yields to efficient global planner capable of solving with a real-time performance problems in geometrically complex environments with moving obstacles.
Abstract: This paper presents a path planner for robots operating in dynamically changing environments with both static and moving obstacles. The proposed planner is based on probabilistic path planning techniques and it combines techniques originally designed for solving multiple-query and single-query problems. The planner first starts with a preprocessing stage that constructs a roadmap of valid paths with respect to the static obstacles. It then uses lazy-evaluation mechanisms combined with a single-query technique as local planner in order to rapidly update the roadmap according to the dynamic changes. This allows to answer queries quickly when the moving obstacles have little impact on the free-space connectivity. When the solution can not be found in the updated roadmap, the planner initiates a reinforcement stage that possibly results into the creation of cycles representing alternative paths that were not already stored in the roadmap. Simulation results show that this combination of techniques yields to efficient global planner capable of solving with a real-time performance problems in geometrically complex environments with moving obstacles.

Proceedings ArticleDOI
28 Sep 2004
TL;DR: A novel and integrated approach that combines autonomous exploration with simultaneous localization and mapping that uses a grid-based version of the FastSLAM algorithm and at each point in time considers actions to actively close loops during exploration.
Abstract: Acquiring models of the environment belongs to the fundamental tasks of mobile robots. In the last few years several researchers have focused on the problem of simultaneous localization and mapping (SLAM). Classic SLAM approaches are passive in the sense that they only process the perceived sensor data and do not influence the motion of the mobile robot. In this paper we present a novel and integrated approach that combines autonomous exploration with simultaneous localization and mapping. Our method uses a grid-based version of the FastSLAM algorithm and at each point in time considers actions to actively close loops during exploration. By re-entering already visited areas the robot reduces its localization error and this way learns more accurate maps. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches lacking the ability to actively close loops.

Posted Content
TL;DR: The approach to solving the motion-planning problem in mobile robot control using neural networks-based technique and the method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks.
Abstract: This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the "free" space using ultrasound range finder data. The second neural network "finds" a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

Proceedings ArticleDOI
07 Jun 2004
TL;DR: This paper focuses on analyzing the tradeoffs between maintaining dynamic roadmaps and applying an on-line bidirectional rapidly-exploring random tree (RRT) planner alone, which requires no preprocessing or maintenance.
Abstract: We evaluate the use of dynamic roadmaps for online motion planning in changing environments. When changes are detected in the workspace, the validity state of affected edges and nodes of a precompiled roadmap are updated accordingly. We concentrate in this paper on analyzing the tradeoffs between maintaining dynamic roadmaps and applying an on-line bidirectional rapidly-exploring random tree (RRT) planner alone, which requires no preprocessing or maintenance. We ground the analysis in several benchmarks in virtual environments with randomly moving obstacles. Different robotics structures are used, including a 17 degrees of freedom model of NASA's Robonaut humanoid. Our results show that dynamic roadmaps can be both faster and more capable for planning difficult motions than using on-line planning alone. In particular, we investigate its scalability to 3D workspaces and higher dimensional configurations spaces, as our main interest is the application of the method to interactive domains involving humanoids.

Journal ArticleDOI
TL;DR: In this article, the authors report on the objective of the motion planning problem well known in robotics, which consists of the ability of a mobile robot to plan and execute collision-free motions within its environment.
Abstract: Over the last few years, a number of studies were reported concerning a machine learning, and how it has been applied to help mobile robots to improve their operational capabilities. One of the most important issues in the design and development of intelligent mobile system is the navigation problem. This consists of the ability of a mobile robot to plan and execute collision-free motions within its environment. However, this environment may be imprecise, vast, dynamical and either partially or non-structure d. Robots must be able to understand the structure of this environment. To reach their targets without collisions, the robots must be en dowed with perception, data processing, recognition, learning, reasoning, interpreting, decision-making and action capacities. The ability to acquire these faculties to treat and transmit knowledge constitutes the key of a certain kind of artificial intelligence. Reproduce this kind of intelligence is, up to now, a human ambition in the construction and development of intelligent machines, and particularly autonomous mobile robots. To reach a reasonable degree of autonomy two basic requirements are sensing and reasoning. The former is provided by on-board sensory system that gathers information about robot with respect to the surrounding scene. The later is accomplished by devising algorithms that exploit this information in order to generate appropriate commands for robot. And with this algorithm we will deal in this chapter. We report on the objective of the motion planning problem well known in robotics. Given

Journal ArticleDOI
TL;DR: In this paper, a relation between the topological properties of a configuration space (the structure of its cohomology algebra) and the character of instabilities, which are unavoidable in any motion planning algorithm, is found.

Proceedings ArticleDOI
16 Aug 2004
TL;DR: This paper presents a receding horizon controller that can be used to design trajectories for an aerial vehicle flying through a three dimensional terrain with obstacles and no-fly zones and introduces a new cost-to-go function that includes an altitude penalty and accounts for the vehicle dynamics.
Abstract: This paper presents a receding horizon controller (RHC) that can be used to design trajectories for an aerial vehicle flying through a three dimensional terrain with obstacles and no-fly zones. To avoid exposure to threats, the paths are chosen to stay as close to the terrain as possible, but the vehicle can choose to pop-up over the obstacles if necessary. The approach is similar to our previous two-dimensional algorithms that construct a coarse cost map to provide approximate paths from a sparse set of nodes to the goal and then use Mixed-integer Linear Programming (MILP) optimization to design a detailed trajectory. The main contribution of this paper is to extend this approach to 3D, in particular providing a new algorithm for connecting the cost map and the detailed path in the MILP. This connection is done by introducing a new cost-to-go function that includes an altitude penalty and accounts for the vehicle dynamics. Initial guess for MILP RHC is constructed from the previous solution and is shown to reduce the solution time. Several simulation results are presented to show that the path planning algorithm yields good overall performance and is computationally tractable in a complex environment.

Proceedings ArticleDOI
06 Jul 2004
TL;DR: A simple, randomized procedure that performs value update steps that strictly improve the value of all belief points in each step that belongs to the family of point-based value iteration solution techniques for POMDP.
Abstract: We present an approximate POMDP solution method for robot planning in partially observable environments. Our algorithm belongs to the family of point-based value iteration solution techniques for POMDP, in which planning is performed only on a sampled set of reachable belief points. We describe a simple, randomized procedure that performs value update steps that strictly improve the value of all belief points in each step. We demonstrate our algorithm on a robotic delivery task in an office environment and on several benchmark problems, for which we compute solutions that are very competitive to those of state-of-the-art methods in terms of speed and solution quality.